





'''simple docstring'''



import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


_SCREAMING_SNAKE_CASE       =     [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]




def  _lowerCAmelCase    (					lowerCamelCase_      :	Optional[Any]					):
			for pegasus_name, hf_name in PATTERNS:
						__lowercase					    =				k.replace(UpperCamelCase_		,							UpperCamelCase_					)
			return k




def  _lowerCAmelCase    (					lowerCamelCase_      :	dict		,							lowerCamelCase_      :	dict					):
			__lowercase					    =				DEFAULTS.copy()
			cfg_kwargs.update(UpperCamelCase_					)
			__lowercase					    =				PegasusConfig(**UpperCamelCase_					)
			__lowercase					    =				PegasusForConditionalGeneration(UpperCamelCase_					)
			__lowercase					    =				torch_model.model.state_dict()
			__lowercase					    =				{}
			for k, v in tf_weights.items():
						__lowercase					    =				rename_state_dict_key(UpperCamelCase_					)
						if new_k not in sd:
									raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})"					)

						if "dense" in k or "proj" in new_k:
									__lowercase					    =				v.T
						__lowercase					    =				torch.tensor(UpperCamelCase_		,							dtype=sd[new_k].dtype					)
						assert v.shape == sd[new_k].shape, f"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
			# make sure embedding.padding_idx is respected
			__lowercase					    =				torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]					)
			__lowercase					    =				mapping['''shared.weight''']
			__lowercase					    =				mapping['''shared.weight''']
			__lowercase					    =				{k: torch.zeros_like(UpperCamelCase_					) for k, v in sd.items() if k.endswith('''bias'''					) and k not in mapping}
			mapping.update(**UpperCamelCase_					)
			__lowercase    ,		__lowercase					    =				torch_model.model.load_state_dict(UpperCamelCase_		,							strict=UpperCamelCase_					)
			__lowercase					    =				[
			    k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
			]
			assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
			assert extra == [], f"no matches found for the following tf keys {extra}"
			return torch_model




def  _lowerCAmelCase    (					lowerCamelCase_      :	List[str]="./ckpt/aeslc/model.ckpt-32000"					):
			__lowercase					    =				tf.train.list_variables(UpperCamelCase_					)
			__lowercase					    =				{}
			__lowercase					    =				['''Adafactor''', '''global_step''']
			for name, shape in tqdm(UpperCamelCase_		,							desc='''converting tf checkpoint to dict'''					):
						__lowercase					    =				any(pat in name for pat in ignore_name					)
						if skip_key:
									continue
						__lowercase					    =				tf.train.load_variable(UpperCamelCase_		,							UpperCamelCase_					)
						__lowercase					    =				array
			return tf_weights




def  _lowerCAmelCase    (					lowerCamelCase_      :	str		,							lowerCamelCase_      :	str					):
			# save tokenizer first
			__lowercase					    =				Path(UpperCamelCase_					).parent.name
			__lowercase					    =				task_specific_params[f"summarization_{dataset}"]['''max_position_embeddings''']
			__lowercase					    =				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''		,							model_max_length=UpperCamelCase_					)
			assert tok.model_max_length == desired_max_model_length
			tok.save_pretrained(UpperCamelCase_					)

			# convert model
			__lowercase					    =				get_tf_weights_as_numpy(UpperCamelCase_					)
			__lowercase					    =				task_specific_params[f"summarization_{dataset}"]
			if dataset == "large":
						__lowercase					    =				task_specific_params
			__lowercase					    =				convert_pegasus(UpperCamelCase_		,							UpperCamelCase_					)
			torch_model.save_pretrained(UpperCamelCase_					)
			__lowercase					    =				torch_model.state_dict()
			sd.pop('''model.decoder.embed_positions.weight'''					)
			sd.pop('''model.encoder.embed_positions.weight'''					)
			torch.save(UpperCamelCase_		,							Path(UpperCamelCase_					) / '''pytorch_model.bin'''					)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE       =     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
	parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
	_SCREAMING_SNAKE_CASE       =     parser.parse_args()
	if args.save_dir is None:
		_SCREAMING_SNAKE_CASE       =     Path(args.tf_ckpt_path).parent.name
		_SCREAMING_SNAKE_CASE       =     os.path.join('''pegasus''', dataset)
	convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)










'''simple docstring'''

from __future__ import annotations

SCREAMING_SNAKE_CASE__     :						List[Any]	=  1.6021e-19  # units = C






def 				a  (     UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					)       ->	tuple[str, float]:
		if (conductivity, electron_conc, mobility).count(0			) != 1:
				raise ValueError('You cannot supply more or less than 2 values'			)
		elif conductivity < 0:
				raise ValueError('Conductivity cannot be negative'			)
		elif electron_conc < 0:
				raise ValueError('Electron concentration cannot be negative'			)
		elif mobility < 0:
				raise ValueError('mobility cannot be negative'			)
		elif conductivity == 0:
				return (
				    "conductivity",
				    mobility * electron_conc * ELECTRON_CHARGE,
				)
		elif electron_conc == 0:
				return (
				    "electron_conc",
				    conductivity / (mobility * ELECTRON_CHARGE),
				)
		else:
				return (
				    "mobility",
				    conductivity / (electron_conc * ELECTRON_CHARGE),
				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




